import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def 			lowercase_				(			):
    lowercase__ :   Dict    					=				"https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
    lowercase__ :   Optional[Any]    					=				Image.open(requests.get(_lowerCamelCase		,	stream=_lowerCamelCase).raw).convert("RGB")

    return image

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]):
    lowercase__ :   int    					=				[]
    # fmt: off

    # vision encoder
    rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding"))
    rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding"))
    rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight"))
    rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias"))
    rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight"))
    rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias"))

    for i in range(config.vision_config.num_hidden_layers):
        rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',))
        rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight'''))
        rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias'''))

    # QFormer
    rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight"))
    rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias"))

    # fmt: on
    return rename_keys

def 			lowercase_				(			_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       Tuple		,	_lowerCamelCase							:       List[str]):
    lowercase__ :   Dict    					=				dct.pop(_lowerCamelCase)
    lowercase__ :   Optional[Any]    					=				val

def 			lowercase_				(			_lowerCamelCase							:       Optional[Any]		,	_lowerCamelCase							:       Optional[int]):
    for i in range(config.vision_config.num_hidden_layers):
        # read in original q and v biases
        lowercase__ :   Union[str, Any]    					=				state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''')
        lowercase__ :   str    					=				state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''')

        # next, set bias in the state dict
        lowercase__ :   Dict    					=				torch.cat((q_bias, torch.zeros_like(_lowerCamelCase		,	requires_grad=_lowerCamelCase), v_bias))
        lowercase__ :   int    					=				qkv_bias

def 			lowercase_				(			_lowerCamelCase							:       str):
    lowercase__ :   int    					=				364 if "coco" in model_name else 224
    lowercase__ :   str    					=				InstructBlipVisionConfig(image_size=_lowerCamelCase).to_dict()

    # make sure the models have proper bos_token_id and eos_token_id set (important for generation)
    # seems like flan-T5 models don't have bos_token_id properly set?
    if "t5-xl" in model_name:
        lowercase__ :   List[str]    					=				TaConfig.from_pretrained("google/flan-t5-xl"		,	dense_act_fn="gelu"		,	bos_token_id=1).to_dict()
    elif "t5-xxl" in model_name:
        lowercase__ :   Union[str, Any]    					=				TaConfig.from_pretrained("google/flan-t5-xxl"		,	dense_act_fn="gelu"		,	bos_token_id=1).to_dict()
    elif "vicuna-7b" in model_name:
        lowercase__ :   Any    					=				LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf"		,	vocab_size=3_2001).to_dict()
    elif "vicuna-13b" in model_name:
        lowercase__ :   List[str]    					=				LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf"		,	vocab_size=3_2001).to_dict()
    else:
        raise ValueError("Model name not supported")

    # the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
    lowercase__ :   Optional[int]    					=				InstructBlipQFormerConfig(vocab_size=3_0523).to_dict()
    lowercase__ :   str    					=				InstructBlipConfig(vision_config=_lowerCamelCase		,	text_config=_lowerCamelCase		,	qformer_config=_lowerCamelCase)

    return config, image_size

@torch.no_grad()
def 			lowercase_				(			_lowerCamelCase							:       Dict		,	_lowerCamelCase							:       Optional[Any]=None		,	_lowerCamelCase							:       Tuple=False):
    lowercase__ :   Optional[int]    					=				AutoTokenizer.from_pretrained("bert-base-uncased"		,	truncation_side="left")
    qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"})

    if "t5" in model_name:
        lowercase__ :   Any    					=				TaTokenizerFast.from_pretrained("google/flan-t5-xl"		,	truncation_side="left")
    elif "vicuna" in model_name:
        # the following was used in the original implementation:
        # tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
        # tokenizer.add_special_tokens({"pad_token": "[PAD]"})
        # tokenizer.add_special_tokens({"bos_token": "</s>"})
        # tokenizer.add_special_tokens({"eos_token": "</s>"})
        # tokenizer.add_special_tokens({"unk_token": "</s>"})
        lowercase__ :   List[Any]    					=				LlamaTokenizerFast.from_pretrained(
            "huggyllama/llama-7b"		,	truncation_side="left"		,	bos_token="</s>"		,	unk_token="</s>")
        tokenizer.add_special_tokens({"pad_token": "[PAD]"})

    lowercase__					,   lowercase__ :   Union[str, Any]    					=				get_blipa_config(_lowerCamelCase)
    lowercase__ :   Any    					=				InstructBlipForConditionalGeneration(_lowerCamelCase).eval()

    lowercase__ :   List[Any]    					=				{
        "instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
        "instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
        "instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
        "instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
    }

    lowercase__					,   lowercase__ :   Union[str, Any]    					=				model_name_to_original[model_name]

    # load original model
    print("Loading original model...")
    lowercase__ :   Any    					=				"cuda:1" if torch.cuda.is_available() else "cpu"
    lowercase__ :   Any    					=				"cuda:2" if torch.cuda.is_available() else "cpu"
    lowercase__					,   lowercase__					,   lowercase__ :   Any    					=				load_model_and_preprocess(
        name=_lowerCamelCase		,	model_type=_lowerCamelCase		,	is_eval=_lowerCamelCase		,	device=_lowerCamelCase)
    original_model.eval()
    print("Done!")

    # update state dict keys
    lowercase__ :   Dict    					=				original_model.state_dict()
    lowercase__ :   Tuple    					=				create_rename_keys(_lowerCamelCase)
    for src, dest in rename_keys:
        rename_key(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

    # some keys can be renamed efficiently
    for key, val in state_dict.copy().items():
        lowercase__ :   List[Any]    					=				state_dict.pop(_lowerCamelCase)
        if key.startswith("Qformer.bert"):
            lowercase__ :   List[str]    					=				key.replace("Qformer.bert"		,	"qformer")
        if "attention.self" in key:
            lowercase__ :   Dict    					=				key.replace("self"		,	"attention")
        if "llm_proj" in key:
            lowercase__ :   Union[str, Any]    					=				key.replace("llm_proj"		,	"language_projection")
        if "t5_proj" in key:
            lowercase__ :   Optional[Any]    					=				key.replace("t5_proj"		,	"language_projection")
        if key.startswith("llm_model"):
            lowercase__ :   Optional[Any]    					=				key.replace("llm_model"		,	"language_model")
        if key.startswith("t5"):
            lowercase__ :   List[str]    					=				key.replace("t5"		,	"language")
        lowercase__ :   Dict    					=				val

    # read in qv biases
    read_in_q_v_bias(_lowerCamelCase		,	_lowerCamelCase)

    # note: weights get loaded in torch.float32 by default
    hf_model.load_state_dict(_lowerCamelCase		,	strict=_lowerCamelCase)

    lowercase__ :   Any    					=				load_demo_image()
    lowercase__ :   Optional[Any]    					=				"What is unusual about this image?"

    # create processor
    lowercase__ :   str    					=				BlipImageProcessor(
        size={"height": image_size, "width": image_size}		,	image_mean=_lowerCamelCase		,	image_std=_lowerCamelCase)
    lowercase__ :   int    					=				InstructBlipProcessor(
        image_processor=_lowerCamelCase		,	tokenizer=_lowerCamelCase		,	qformer_tokenizer=_lowerCamelCase		,	)
    lowercase__ :   Tuple    					=				processor(images=_lowerCamelCase		,	text=_lowerCamelCase		,	return_tensors="pt").to(_lowerCamelCase)

    # make sure processor creates exact same pixel values
    lowercase__ :   Optional[int]    					=				vis_processors["eval"](_lowerCamelCase).unsqueeze(0).to(_lowerCamelCase)
    lowercase__ :   List[str]    					=				inputs.pixel_values
    assert torch.allclose(original_pixel_values.to(pixel_values.device)		,	_lowerCamelCase)

    original_model.to(_lowerCamelCase)
    hf_model.to(_lowerCamelCase)
    with torch.no_grad():
        if "vicuna" in model_name:
            lowercase__ :   List[Any]    					=				original_model({"image": original_pixel_values, "text_input": [prompt]}).logits
            lowercase__ :   Union[str, Any]    					=				hf_model(**_lowerCamelCase).logits
        else:
            lowercase__ :   str    					=				original_model(
                {"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]}).logits
            lowercase__ :   List[str]    					=				tokenizer("\n"		,	return_tensors="pt").input_ids.to(_lowerCamelCase)
            lowercase__ :   List[Any]    					=				label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id		,	-100)
            lowercase__ :   Dict    					=				hf_model(**_lowerCamelCase		,	labels=_lowerCamelCase).logits

    print("First values of original logits:"		,	original_logits[0, :3, :3])
    print("First values of HF logits:"		,	logits[0, :3, :3])

    # assert values
    assert original_logits.shape == logits.shape
    lowercase__ :   Union[str, Any]    					=				1E-4 if "vicuna" in model_name else 1E-5
    assert torch.allclose(original_logits.to(logits.device)		,	_lowerCamelCase		,	atol=_lowerCamelCase)
    print("Looks ok!")

    print("Generating with original model...")
    lowercase__ :   Union[str, Any]    					=				original_model.generate({"image": original_pixel_values, "prompt": prompt}		,	num_beams=5)

    # important: we need to cast the weights of the HF model to the appropriate type
    print("Generating with HF model...")
    lowercase__ :   List[str]    					=				hf_model.generate(
        **_lowerCamelCase		,	do_sample=_lowerCamelCase		,	num_beams=5		,	max_length=256		,	min_length=1		,	top_p=0.9		,	repetition_penalty=1.5		,	length_penalty=1.0		,	temperature=1		,	)
    if "vicuna" in model_name:
        # convert output id 0 to 2 (eos_token_id)
        # TODO add this in the generate method?
        lowercase__ :   Optional[Any]    					=				2
    print("Original generation:"		,	_lowerCamelCase)
    lowercase__ :   int    					=				processor.batch_decode(_lowerCamelCase		,	skip_special_tokens=_lowerCamelCase)
    lowercase__ :   Union[str, Any]    					=				[text.strip() for text in output_text]
    print("HF generation:"		,	_lowerCamelCase)

    if pytorch_dump_folder_path is not None:
        processor.save_pretrained(_lowerCamelCase)
        hf_model.save_pretrained(_lowerCamelCase)

    if push_to_hub:
        processor.push_to_hub(f'''Salesforce/{model_name}''')
        hf_model.push_to_hub(f'''Salesforce/{model_name}''')


if __name__ == "__main__":
      UpperCamelCase  							=						argparse.ArgumentParser()
      UpperCamelCase  							=						[
          '''instructblip-vicuna-7b''',
          '''instructblip-vicuna-13b''',
          '''instructblip-flan-t5-xl''',
          '''instructblip-flan-t5-xxl''',
      ]
      parser.add_argument(
          '''--model_name''',
          default='''instructblip-flan-t5-xl''',
          choices=choices,
          type=str,
          help='''Path to hf config.json of model to convert''',
      )
      parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
      parser.add_argument(
          '''--push_to_hub''',
          action='''store_true''',
          help='''Whether to push the model and processor to the hub after converting''',
      )

      UpperCamelCase  							=						parser.parse_args()

      convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""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
		_lowercase : List[Any]      						=						"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      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Optional[Any]=None							,		__lowerCamelCase				:    str=None							,		__lowerCamelCase				:    Tuple=None							,		__lowerCamelCase				:    List[Any]=None							,		__lowerCamelCase				:    Tuple=None							,		__lowerCamelCase				:    str=None							,		):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__		:		Any			=np.where(input_ids != config.pad_token_id							,		1							,		0	)
			if decoder_attention_mask is None:
						lowerCamelCase__		:		str			=np.where(decoder_input_ids != config.pad_token_id							,		1							,		0	)
			if head_mask is None:
						lowerCamelCase__		:		Dict			=np.ones((config.encoder_layers, config.encoder_attention_heads)	)
			if decoder_head_mask is None:
						lowerCamelCase__		:		str			=np.ones((config.decoder_layers, config.decoder_attention_heads)	)
			if cross_attn_head_mask is None:
						lowerCamelCase__		:		Dict			=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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  str,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  List[str]=13,  lowerCamelCase							:  Dict=7,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  int=99,  lowerCamelCase							:  Union[str, Any]=16,  lowerCamelCase							:  List[str]=2,  lowerCamelCase							:  int=4,  lowerCamelCase							:  Tuple=4,  lowerCamelCase							:  Optional[Any]="gelu",  lowerCamelCase							:  List[str]=0.1,  lowerCamelCase							:  str=0.1,  lowerCamelCase							:  Optional[int]=32,  lowerCamelCase							:  List[str]=2,  lowerCamelCase							:  Tuple=1,  lowerCamelCase							:  Optional[int]=0,  lowerCamelCase							:  int=0.02,  )->					Optional[Any]:
								lowerCamelCase__		:		List[str]			=parent
								lowerCamelCase__		:		Dict			=batch_size
								lowerCamelCase__		:		Optional[int]			=seq_length
								lowerCamelCase__		:		Any			=is_training
								lowerCamelCase__		:		Optional[int]			=use_labels
								lowerCamelCase__		:		List[str]			=vocab_size
								lowerCamelCase__		:		List[Any]			=hidden_size
								lowerCamelCase__		:		List[Any]			=num_hidden_layers
								lowerCamelCase__		:		Tuple			=num_attention_heads
								lowerCamelCase__		:		List[Any]			=intermediate_size
								lowerCamelCase__		:		Union[str, Any]			=hidden_act
								lowerCamelCase__		:		Optional[Any]			=hidden_dropout_prob
								lowerCamelCase__		:		Tuple			=attention_probs_dropout_prob
								lowerCamelCase__		:		Optional[Any]			=max_position_embeddings
								lowerCamelCase__		:		List[Any]			=eos_token_id
								lowerCamelCase__		:		Tuple			=pad_token_id
								lowerCamelCase__		:		Union[str, Any]			=bos_token_id
								lowerCamelCase__		:		List[Any]			=initializer_range



					def 		snake_case       (       self							:  Optional[Any]     )->					str:
								lowerCamelCase__		:		Dict			=np.clip(ids_tensor([self.batch_size, self.seq_length - 1],  self.vocab_size     ),  3,  self.vocab_size     )
								lowerCamelCase__		:		Union[str, Any]			=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),  dtype=np.intaa     )),  -1     )

								lowerCamelCase__		:		Dict			=shift_tokens_right(lowerCamelCase,  1,  2     )

								lowerCamelCase__		:		Optional[Any]			=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,  )
								lowerCamelCase__		:		List[str]			=prepare_blenderbot_inputs_dict(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     )
								return config, inputs_dict



					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=self.prepare_config_and_inputs()
								return config, inputs_dict



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Tuple     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=20
								lowerCamelCase__		:		Optional[int]			=model_class_name(lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=model.encode(inputs_dict['''input_ids''']     )

								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

								lowerCamelCase__		:		Any			=model.init_cache(decoder_input_ids.shape[0],  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Dict			=jnp.ones((decoder_input_ids.shape[0], max_decoder_length),  dtype='''i4'''     )

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

								lowerCamelCase__		:		Optional[int]			=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],  dtype='''i4'''     )
								lowerCamelCase__		:		Union[str, Any]			=model.decode(
								    decoder_input_ids[:, -1:],  lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  past_key_values=outputs_cache.past_key_values,  decoder_position_ids=lowerCamelCase,  )

								lowerCamelCase__		:		int			=model.decode(lowerCamelCase,  lowerCamelCase     )

								lowerCamelCase__		:		Dict			=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 		snake_case       (       self							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  str     )->					List[str]:
								lowerCamelCase__		:		List[Any]			=20
								lowerCamelCase__		:		List[Any]			=model_class_name(lowerCamelCase     )

								lowerCamelCase__		:		Union[str, Any]			=model.encode(inputs_dict['''input_ids''']     )

								lowerCamelCase__	,   lowerCamelCase__		:		str			=(
								    inputs_dict['''decoder_input_ids'''],
								    inputs_dict['''decoder_attention_mask'''],
								)

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

								lowerCamelCase__		:		List[str]			=model.init_cache(decoder_input_ids.shape[0],  lowerCamelCase,  lowerCamelCase     )
								lowerCamelCase__		:		Union[str, Any]			=jnp.broadcast_to(
								    jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :],  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),  )

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

								lowerCamelCase__		:		Optional[Any]			=model.decode(lowerCamelCase,  lowerCamelCase,  decoder_attention_mask=lowerCamelCase     )

								lowerCamelCase__		:		List[Any]			=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        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							9_9



					def 		snake_case       (       self							:  Optional[int]     )->					Optional[Any]:
								lowerCamelCase__		:		Union[str, Any]			=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,  )

								lowerCamelCase__		:		Any			=input_ids.shape[0]
								lowerCamelCase__		:		Any			=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 		snake_case       (       self							:  Any     )->					Union[str, Any]:
								lowerCamelCase__	,   lowerCamelCase__	,   lowerCamelCase__		:		List[Any]			=self._get_config_and_data()
								lowerCamelCase__		:		int			=FlaxBlenderbotForConditionalGeneration(lowerCamelCase     )
								lowerCamelCase__		:		str			=lm_model(input_ids=lowerCamelCase     )
								lowerCamelCase__		:		List[Any]			=(batch_size, input_ids.shape[1], config.vocab_size)
								self.assertEqual(outputs['''logits'''].shape,  lowerCamelCase     )



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




					def 		snake_case       (       self							:  Union[str, Any]     )->					Union[str, Any]:
								lowerCamelCase__		:		Optional[int]			=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],  dtype=np.intaa     )
								lowerCamelCase__		:		Optional[Any]			=shift_tokens_right(lowerCamelCase,  1,  2     )
								lowerCamelCase__		:		str			=np.equal(lowerCamelCase,  1     ).astype(np.floataa     ).sum()
								lowerCamelCase__		:		List[str]			=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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   unittest.TestCase		,   lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							True
					_a   			=							(
					    (
					        FlaxBlenderbotModel,
					        FlaxBlenderbotForConditionalGeneration,
					    )
					    if is_flax_available()
					    else ()
					)
					_a   			=							(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[str]:
								lowerCamelCase__		:		str			=FlaxBlenderbotModelTester(self     )



					def 		snake_case       (       self							:  Optional[int]     )->					int:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=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 		snake_case       (       self							:  List[str]     )->					Union[str, Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Union[str, Any]			=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 		snake_case       (       self							:  List[Any]     )->					Tuple:
								lowerCamelCase__	,   lowerCamelCase__		:		int			=self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__     ):
														lowerCamelCase__		:		List[Any]			=self._prepare_for_class(lowerCamelCase,  lowerCamelCase     )
														lowerCamelCase__		:		int			=model_class(lowerCamelCase     )

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

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

														with self.subTest('''JIT Disabled'''     ):
																	with jax.disable_jit():
																				lowerCamelCase__		:		Dict			=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 		snake_case       (       self							:  List[str]     )->					Dict:
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCamelCase__		:		Optional[int]			={
														    '''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							:  Union[str, Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  Tuple     ):
																	return model.decode(
																	    decoder_input_ids=lowerCamelCase,  decoder_attention_mask=lowerCamelCase,  encoder_outputs=lowerCamelCase,  )

														with self.subTest('''JIT Enabled'''     ):
																	lowerCamelCase__		:		Union[str, Any]			=decode_jitted(**lowerCamelCase     ).to_tuple()

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




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

								lowerCamelCase__		:		Tuple			=FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''',  from_pt=lowerCamelCase     )
								lowerCamelCase__		:		int			=BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''     )

								lowerCamelCase__		:		str			=['''Sam''']
								lowerCamelCase__		:		Union[str, Any]			=tokenizer(lowerCamelCase,  return_tensors='''jax'''     )

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

								lowerCamelCase__		:		Union[str, Any]			=tokenizer.batch_decode(lowerCamelCase,  **lowerCamelCase     )
								assert generated_txt[0].strip() == tgt_text




