import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp

def 	__lowerCamelCase							(				__a    : List[Any]      ,							__a    : List[Any]      ,							__a    : int      ,							__a    : Optional[int]      ,							__a    : int = 4_096		)      ->       Optional[Any]:
	_lowercase   ,  _lowercase   ,  _lowercase   =key.shape[-3:]
	_lowercase   =value.shape[-1]
	_lowercase   =min(__a      ,							__a		)
	_lowercase   =query / jnp.sqrt(__a		)

	@functools.partial(jax.checkpoint      ,							prevent_cse=__a		)
	def summarize_chunk(__a    : Dict      ,							__a    : Optional[Any]      ,							__a    : Optional[int]		):
		_lowercase   =jnp.einsum("...qhd,...khd->...qhk"      ,							__a      ,							__a      ,							precision=__a		)

		_lowercase   =jnp.max(__a      ,							axis=-1      ,							keepdims=__a		)
		_lowercase   =jax.lax.stop_gradient(__a		)
		_lowercase   =jnp.exp(attn_weights - max_score		)

		_lowercase   =jnp.einsum("...vhf,...qhv->...qhf"      ,							__a      ,							__a      ,							precision=__a		)
		_lowercase   =jnp.einsum("...qhk->...qh"      ,							__a		)

		return (exp_values, exp_weights.sum(axis=-1		), max_score)

	def chunk_scanner(__a    : str		):
		# julienne key array
		_lowercase   =jax.lax.dynamic_slice(
		    operand=__a      ,							start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]      ,							slice_sizes=list(key.shape[:-3]		) + [key_chunk_size, num_heads, k_features]      ,							)

		# julienne value array
		_lowercase   =jax.lax.dynamic_slice(
		    operand=__a      ,							start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]      ,							slice_sizes=list(value.shape[:-3]		) + [key_chunk_size, num_heads, v_features]      ,							)

		return summarize_chunk(__a      ,							__a      ,							__a		)

	_lowercase   ,  _lowercase   ,  _lowercase   =jax.lax.map(f=__a      ,							xs=jnp.arange(0      ,							__a      ,							__a		)		)

	_lowercase   =jnp.max(__a      ,							axis=0      ,							keepdims=__a		)
	_lowercase   =jnp.exp(chunk_max - global_max		)

	chunk_values *= jnp.expand_dims(__a      ,							axis=-1		)
	chunk_weights *= max_diffs

	_lowercase   =chunk_values.sum(axis=0		)
	_lowercase   =jnp.expand_dims(__a      ,							-1		).sum(axis=0		)

	return all_values / all_weights

def 	__lowerCamelCase							(				__a    : Union[str, Any]      ,							__a    : Dict      ,							__a    : int      ,							__a    : Any=jax.lax.Precision.HIGHEST      ,							__a    : int = 1_024      ,							__a    : int = 4_096		)      ->       Any:
	_lowercase   ,  _lowercase   ,  _lowercase   =query.shape[-3:]

	def chunk_scanner(__a    : List[Any]      ,							__a    : Union[str, Any]		):
		# julienne query array
		_lowercase   =jax.lax.dynamic_slice(
		    operand=__a      ,							start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]      ,							slice_sizes=list(query.shape[:-3]		) + [min(__a      ,							__a		), num_heads, q_features]      ,							)

		return (
		    chunk_idx + query_chunk_size,  # unused ignore it
		    _query_chunk_attention(
		        query=__a      ,							key=__a      ,							value=__a      ,							precision=__a      ,							key_chunk_size=__a		),
		)

	_lowercase   ,  _lowercase   =jax.lax.scan(
	    f=__a      ,							init=0      ,							xs=__a      ,							length=math.ceil(num_q / query_chunk_size		)		)

	return jnp.concatenate(__a      ,							axis=-3		)  # fuse the chunked result back
class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					8
	__SCREAMING_SNAKE_CASE              =					64
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					jnp.floataa


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.dim_head * self.heads
		_lowercase   =self.dim_head**-0.5

		# Weights were exported with old names {to_q, to_k, to_v, to_out}
		_lowercase   =nn.Dense(lowerCAmelCase_       ,  use_bias=lowerCAmelCase_       ,  dtype=self.dtype       ,  name="to_q"       )
		_lowercase   =nn.Dense(lowerCAmelCase_       ,  use_bias=lowerCAmelCase_       ,  dtype=self.dtype       ,  name="to_k"       )
		_lowercase   =nn.Dense(lowerCAmelCase_       ,  use_bias=lowerCAmelCase_       ,  dtype=self.dtype       ,  name="to_v"       )

		_lowercase   =nn.Dense(self.query_dim       ,  dtype=self.dtype       ,  name="to_out_0"       )
		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   ,  _lowercase   =tensor.shape
		_lowercase   =self.heads
		_lowercase   =tensor.reshape(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  dim // head_size       )
		_lowercase   =jnp.transpose(lowerCAmelCase_       ,  (0, 2, 1, 3)       )
		_lowercase   =tensor.reshape(batch_size * head_size       ,  lowerCAmelCase_       ,  dim // head_size       )
		return tensor


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   ,  _lowercase   ,  _lowercase   =tensor.shape
		_lowercase   =self.heads
		_lowercase   =tensor.reshape(batch_size // head_size       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		_lowercase   =jnp.transpose(lowerCAmelCase_       ,  (0, 2, 1, 3)       )
		_lowercase   =tensor.reshape(batch_size // head_size       ,  lowerCAmelCase_       ,  dim * head_size       )
		return tensor


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ):
		_lowercase   =hidden_states if context is None else context

		_lowercase   =self.query(lowerCAmelCase_       )
		_lowercase   =self.key(lowerCAmelCase_       )
		_lowercase   =self.value(lowerCAmelCase_       )

		_lowercase   =self.reshape_heads_to_batch_dim(lowerCAmelCase_       )
		_lowercase   =self.reshape_heads_to_batch_dim(lowerCAmelCase_       )
		_lowercase   =self.reshape_heads_to_batch_dim(lowerCAmelCase_       )

		if self.use_memory_efficient_attention:
			_lowercase   =query_states.transpose(1       ,  0       ,  2       )
			_lowercase   =key_states.transpose(1       ,  0       ,  2       )
			_lowercase   =value_states.transpose(1       ,  0       ,  2       )

			# this if statement create a chunk size for each layer of the unet
			# the chunk size is equal to the query_length dimension of the deepest layer of the unet

			_lowercase   =query_states.shape[-3]
			if flatten_latent_dim % 64 == 0:
				_lowercase   =int(flatten_latent_dim / 64       )
			elif flatten_latent_dim % 16 == 0:
				_lowercase   =int(flatten_latent_dim / 16       )
			elif flatten_latent_dim % 4 == 0:
				_lowercase   =int(flatten_latent_dim / 4       )
			else:
				_lowercase   =int(lowerCAmelCase_       )

			_lowercase   =jax_memory_efficient_attention(
			    lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  query_chunk_size=lowerCAmelCase_       ,  key_chunk_size=4096 * 4       )

			_lowercase   =hidden_states.transpose(1       ,  0       ,  2       )
		else:
			# compute attentions
			_lowercase   =jnp.einsum("b i d, b j d->b i j"       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			_lowercase   =attention_scores * self.scale
			_lowercase   =nn.softmax(lowerCAmelCase_       ,  axis=2       )

			# attend to values
			_lowercase   =jnp.einsum("b i j, b j d -> b i d"       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =self.reshape_batch_dim_to_heads(lowerCAmelCase_       )
		_lowercase   =self.proj_attn(lowerCAmelCase_       )
		return self.dropout_layer(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					jnp.floataa
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		# self attention (or cross_attention if only_cross_attention is True)
		_lowercase   =FlaxAttention(
		    self.dim       ,  self.n_heads       ,  self.d_head       ,  self.dropout       ,  self.use_memory_efficient_attention       ,  dtype=self.dtype       )
		# cross attention
		_lowercase   =FlaxAttention(
		    self.dim       ,  self.n_heads       ,  self.d_head       ,  self.dropout       ,  self.use_memory_efficient_attention       ,  dtype=self.dtype       )
		_lowercase   =FlaxFeedForward(dim=self.dim       ,  dropout=self.dropout       ,  dtype=self.dtype       )
		_lowercase   =nn.LayerNorm(epsilon=1e-5       ,  dtype=self.dtype       )
		_lowercase   =nn.LayerNorm(epsilon=1e-5       ,  dtype=self.dtype       )
		_lowercase   =nn.LayerNorm(epsilon=1e-5       ,  dtype=self.dtype       )
		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		# self attention
		_lowercase   =hidden_states
		if self.only_cross_attention:
			_lowercase   =self.attna(self.norma(lowerCAmelCase_       )       ,  lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )
		else:
			_lowercase   =self.attna(self.norma(lowerCAmelCase_       )       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =hidden_states + residual

		# cross attention
		_lowercase   =hidden_states
		_lowercase   =self.attna(self.norma(lowerCAmelCase_       )       ,  lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =hidden_states + residual

		# feed forward
		_lowercase   =hidden_states
		_lowercase   =self.ff(self.norma(lowerCAmelCase_       )       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =hidden_states + residual

		return self.dropout_layer(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					1
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					jnp.floataa
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =nn.GroupNorm(num_groups=32       ,  epsilon=1e-5       )

		_lowercase   =self.n_heads * self.d_head
		if self.use_linear_projection:
			_lowercase   =nn.Dense(lowerCAmelCase_       ,  dtype=self.dtype       )
		else:
			_lowercase   =nn.Conv(
			    lowerCAmelCase_       ,  kernel_size=(1, 1)       ,  strides=(1, 1)       ,  padding="VALID"       ,  dtype=self.dtype       ,  )

		_lowercase   =[
		    FlaxBasicTransformerBlock(
		        lowerCAmelCase_       ,  self.n_heads       ,  self.d_head       ,  dropout=self.dropout       ,  only_cross_attention=self.only_cross_attention       ,  dtype=self.dtype       ,  use_memory_efficient_attention=self.use_memory_efficient_attention       ,  )
		    for _ in range(self.depth       )
		]

		if self.use_linear_projection:
			_lowercase   =nn.Dense(lowerCAmelCase_       ,  dtype=self.dtype       )
		else:
			_lowercase   =nn.Conv(
			    lowerCAmelCase_       ,  kernel_size=(1, 1)       ,  strides=(1, 1)       ,  padding="VALID"       ,  dtype=self.dtype       ,  )

		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =hidden_states.shape
		_lowercase   =hidden_states
		_lowercase   =self.norm(lowerCAmelCase_       )
		if self.use_linear_projection:
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  height * width       ,  lowerCAmelCase_       )
			_lowercase   =self.proj_in(lowerCAmelCase_       )
		else:
			_lowercase   =self.proj_in(lowerCAmelCase_       )
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  height * width       ,  lowerCAmelCase_       )

		for transformer_block in self.transformer_blocks:
			_lowercase   =transformer_block(lowerCAmelCase_       ,  lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

		if self.use_linear_projection:
			_lowercase   =self.proj_out(lowerCAmelCase_       )
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
		else:
			_lowercase   =hidden_states.reshape(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )
			_lowercase   =self.proj_out(lowerCAmelCase_       )

		_lowercase   =hidden_states + residual
		return self.dropout_layer(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					jnp.floataa


	def __lowerCAmelCase      (   self       ):
		# The second linear layer needs to be called
		# net_2 for now to match the index of the Sequential layer
		_lowercase   =FlaxGEGLU(self.dim       ,  self.dropout       ,  self.dtype       )
		_lowercase   =nn.Dense(self.dim       ,  dtype=self.dtype       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		_lowercase   =self.net_a(lowerCAmelCase_       ,  deterministic=lowerCAmelCase_       )
		_lowercase   =self.net_a(lowerCAmelCase_       )
		return hidden_states

class 	_a  (							nn.Module					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					42
	__SCREAMING_SNAKE_CASE              =					0.0
	__SCREAMING_SNAKE_CASE              =					jnp.floataa


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.dim * 4
		_lowercase   =nn.Dense(inner_dim * 2       ,  dtype=self.dtype       )
		_lowercase   =nn.Dropout(rate=self.dropout       )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=True       ):
		_lowercase   =self.proj(lowerCAmelCase_       )
		_lowercase   ,  _lowercase   =jnp.split(lowerCAmelCase_       ,  2       ,  axis=2       )
		return self.dropout_layer(hidden_linear * nn.gelu(lowerCAmelCase_       )       ,  deterministic=lowerCAmelCase_       )












"""simple docstring"""



import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


A_     =				'''
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
'''






class 				__SCREAMING_SNAKE_CASE    (					unittest.TestCase							,		UpperCamelCase		):


							def     _UpperCamelCase							(      self   :    int				):
													'''simple docstring'''





													A__    :	Any			=	load_tool("""summarization"""				)
													self.tool.setup()
													A__    :	List[Any]			=	load_tool("""summarization"""      ,     remote=snake_case				)


							def     _UpperCamelCase							(      self   :    Any				):
													'''simple docstring'''





													A__    :	List[Any]			=	self.tool(snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Optional[int]				):
													'''simple docstring'''





													A__    :	List[str]			=	self.remote_tool(snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Dict				):
													'''simple docstring'''





													A__    :	List[Any]			=	self.tool(text=snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )


							def     _UpperCamelCase							(      self   :    Union[str, Any]				):
													'''simple docstring'''





													A__    :	Optional[int]			=	self.remote_tool(text=snake_case				)
													self.assertEqual(
													    snake_case      ,     """Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."""      ,     )






